Dr.N.Mohananthini
@mec.edu.in
Electrical and Electronics Engineering
Muthayammal Engineering College
EDUCATION
B.E., - Electrical & Electronics - Annai Mathammal Sheela Engg. College, Namakkal
M.E., - Applied Electronics - College of Engineering, Guindy
Ph.D., - Electrical - Annamalai University
RESEARCH INTERESTS
Product Development
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
N. Mohananthini, K. Rajeshkumar, and C. Ananth
IOS Press
Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment.
B. Nagarajan, C. Ananth, and N. Mohananthini
Springer Science and Business Media LLC
Nagarajan B, Ananth C, and Mohananthini N
Seventh Sense Research Group Journals
Rajeshkumar K, Ananth C, and Mohananthini N
Seventh Sense Research Group Journals
Nagarajan B, Ananth C, and Mohananthini N
Seventh Sense Research Group Journals
Rajeshkumar K, Ananth C, and Mohananthini N
Seventh Sense Research Group Journals
Kandasamy Rajeshkumar, Chidambaram Ananth, and Natarajan Mohananthini
Engineering, Technology & Applied Science Research
Blockchain (BC) and Machine learning (ML) technologies have been investigated for potential applications in medicine with reasonable success to date. On the other hand, as accurate and early diagnosis of skin lesion classification is essential to gradually increase the survival rate of the patient, Deep-Learning (DL) and ML technologies were introduced for supporting dermatologists to overcome these challenges. This study designed a Blockchain Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using an Optimal Deep Learning (BHESKD-ODL) model. The presented BHESKD-ODL model achieves security and proper classification of skin lesion images using BC to store the medical images of the patients to restrict access to third-party users or intruders. In addition, the BHESKD-ODL method secures the medical images using the mayfly optimization (MFO) algorithm with the Homomorphic Encryption (HE) technique. For skin lesion diagnosis, the proposed BHESKD-ODL method uses pre-processing and the Adam optimizer with a Fully Convolutional Network (FCN) based segmentation process. Furthermore, a radiomics feature extraction with a Bidirectional Recurrent Neural Network (BiRNN) model was employed for skin lesion classification. Finally, the Red Deer Optimization (RDO) algorithm was used for the optimal hyperparameter selection of the BiRNN approach. The experimental results of the BHESKD-ODL system on a benchmark skin dataset proved its promising performance in terms of different measures.
K. Priyanka, N. Mohananthini, S. Saravanan, S. Saranraj, and R. Manikandan
AIP Publishing
N. Mohananthini, M. Y. Mohamed Parvees, and J. Abdul Samath
World Scientific Pub Co Pte Lt
Nowadays, lightweight cryptography attracts academicians, scientists and researchers to concentrate on its requisite with the increasing usage of low resource devices. In this paper, a new lightweight image encryption scheme is proposed using the Lorenz 3D super chaotic map. This encryption scheme is an addition–rotation–XOR block cipher designed for its supremacy, efficacy and speed execution. In this addition–rotation–XOR cipher, the equation for Lorenz 3D chaotic map is iteratively solved to generate double valued signals in a speedy manner using the Runge–Kutta and Euler methods. The addition, rotation and diffusion sequences are generated from the double valued signals, and the source pixels of the 8-bit plain test images are manipulated with the addition, rotation and diffusion of the bytes. Finally, the cipher images are constructed from the manipulated pixels and evaluated with various statistical as well as randomness tests. The results from various tests prove that the proposed chaotic addition–rotation–XOR block image cipher is efficient in terms of randomness and speed.
C. Sowmya, N. Mohananthini, S. Saravanan, and A. Senthil kumar
AIP Publishing
This proposed paper proposes an inverter power control supported on DC-link voltage regulation for Interior Permanent Magnet Synchronous Motor (IPMSM) drives utilizing Artificial Neural Network (ANN) and Fuzzy logic controller. In the proposed method, originally Proportional Integral (PI) Controller and Fuzzy Logic controller was utilized and after that the ANN was used in place of Fuzzy for the similar operation. The analysis was complete with the outputs of the circuit. The torque ripple is abridged by using ANN and Fuzzy. The high gain was achieved by scheming the PI controller and the parameters are simply intended at the required frequency. The high input power and low grid harmonics are efficiently regulated by the inverter power. The DC-link EC can be utilized for the voltage regulation. The usefulness of the proposed method is simulated and the hardware also implemented to attain PF high and the torque ripple has been lowered by Total Harmonic Distortion (THD). The power factor output is 0.98 while the harmonics of grid current as well abridged.This proposed paper proposes an inverter power control supported on DC-link voltage regulation for Interior Permanent Magnet Synchronous Motor (IPMSM) drives utilizing Artificial Neural Network (ANN) and Fuzzy logic controller. In the proposed method, originally Proportional Integral (PI) Controller and Fuzzy Logic controller was utilized and after that the ANN was used in place of Fuzzy for the similar operation. The analysis was complete with the outputs of the circuit. The torque ripple is abridged by using ANN and Fuzzy. The high gain was achieved by scheming the PI controller and the parameters are simply intended at the required frequency. The high input power and low grid harmonics are efficiently regulated by the inverter power. The DC-link EC can be utilized for the voltage regulation. The usefulness of the proposed method is simulated and the hardware also implemented to attain PF high and the torque ripple has been lowered by Total Harmonic Distortion (THD). The power factor output is 0.98 whil...
C. Ananth, M. Karthikeyan, and N. Mohananthini
Springer International Publishing
C. Ananth, , M. Karthikeyan, N. Mohananthini, S. Saravanan, M. Swathisriranjani, , , , and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
An effective multiple watermarking technique supported on neural network into the wavelet transform can be proposed. The wavelet coefficients has been preferred by Human Visual System. In the proposed work focus on Discrete Wavelet Transform based segmented image watermarking techniques using Back-Propagation neural networks. Using improved BPNN, the multiple watermarks are embedded into the original image, which can advance the pace of the learn, reduce the error and the qualified neural networks are extricate multiple watermarks as of the embedded images. The planned strategy achieves a excellent visual effect scheduled the watermarked images as well as high robustness on extracted multiple watermarks.
C Ananth, M Karthikeyan, N Mohananthini, and G Yamuna
American Scientific Publishers
Swathisriranjani M, Mohananthini K, Ranjitha M, Baskar S, and Kavitha D
Institute of Advanced Engineering and Science
<p>In this paper, a problem of allocation and sizing of multiple active power-line conditioners (aplcs) in power systems is handled with novel formulation. The utilized objective function comprises two main factors such as reduction of total harmonic distortion and the total cost of active power-line conditioners (aplcs). The formulated problem is solved by optimization technique SHUFFLE FROG LEAP ALGORITHM(SHFLA) using MATLAB. To evaluate the competence of the proposed formulation, the IEEE 18-bus distorted distribution test system is employed and investigated with various number of aplcs placement. These cases are based on the discrete and limited size for aplcs, requiring the optimization method to solve the constrained and discrete nonlinear problems. The comparison of results in this paper showed that the proposed SHFLA is the most effective result among others in determining optimum location and size of APLC in distribution systems.</p>
Mohananthini Natarajan and Yamuna Govindarajan
Inderscience Publishers
A multiple watermarking scheme based on discrete wavelet transform is presented for the analysis of imperceptibility and robustness. The watermarks are embedded into the detail sub-bands using genetic algorithms for the optimisation to improve the performance of imperceptibility on the watermarked image in terms of peak signal to noise ratio and robustness of extracting watermark in term normalised correlation. The experimental results show that the proposed method achieves good imperceptibility and robustness on various attacks such as noise, filtering, cropping, rotation, translation, histogram equalisation, sharpening, smoothing, intensity transformation, row-column blanking, row-column copying and JPEG compression.